Recent Posts

A practical question in species surveillance is “How much search effort is required for detection?”. This can be quantified under controlled conditions where the number and location of target species are known and participants are recruited to see how success rate varies.
Let’s use an example of an easter egg hunt where the adult (the researcher) wants to quantify how much effort it takes a child (the participant) to find an easter egg.

This short post will describe how to access SILO climatic data for Australia. The data is available as both csv and json, but here we work with the two-dimensional csv format to make use of R’s powerful data table functionality.
At the time of writing there were 18937 stations. We can access the metadata on each station (including location and years of available data), which will later become useful for selecting an appropriate weather station.

How many probability distributions can we generate by imagining simple natural processes? In this post I use a simple binomial random number generator to produce different random variables with a variety of distributions. Using built in probability densities functions in R, I show how the simulated data (plot bars) approach the exact probability density (plot lines) and provide an intuitive interpretation of model parameters of commonly encountered distributions.
A biological example “Nothing in Biology Makes Sense Except in the Light of Evolution” - Theodosius Dobzhansky, 1973

For a given species, a simple mortality response to environmental conditions can represented with the probabilistic outcome (death), which occurs with probabilty \(p\). This simple process is know as a Bernoulli random variable.
A motivating example is how a pest responds to increasing doses of a pesticide. Invertebrate pests cause 10-20% of yield losses in modern food systems. While cultural practices such as crop rotatation and biological control through beneficial insects increasingly form a core component of effective and sustainable management, pesticides remain a widely used tool.

What if a growing population gets eaten by another population?
In a previous post I showed why we might expect a population to grow exponentially when not resource limited. We then extended this to the case where a population reaches some carrying capacity (using a simple and non-mechanistic logistic function).
But population growth can also be curtailed through interactions with another species population, such as a predator. In my area of study, we deal with a lot of herbivorous pests of agricultural systems.

Selected Publications

The evolution of pesticide resistance through space and time is of great economic significance to modern agricultural production systems, and consequently, is often well documented. It can thus be used to dissect the evolutionary and ecological processes that underpin large-scale evolutionary responses.

Mechanistic models of the impacts of climate change on insects can be seen as very specific hypotheses about the connections between microclimate, ecophysiology and vital rates. These models must adequately capture stage-specific responses, carry-over effects between successive stages, and the evolutionary potential of the functional traits involved in complex insect life-cycles. Here we highlight key considerations for current approaches to mechanistic modelling of insect responses to climate change.

Insects are typified by their small size, large numbers, impressive reproductive output and rapid growth. However, insect growth is not simply rapid; rather, insects follow a qualitatively distinct trajectory to many other animals. Here we present a mechanistic growth model for insects and show that increasing specific assimilation during the growth phase can explain the near-exponential growth trajectory of insects.

The uptake of resources from the environment is a basic feature of all life. Consumption rate has been found to scale with body size with an exponent close to unity across diverse organisms. However, past analyses have ignored the important distinction between ontogenetic and interspecific size comparisons. Using principles of dynamic energy budget theory, we present a mechanistic model for the body mass scaling of consumption, which separates interspecific size effects from ontogenetic size effects.

Design constraints imposed by increasing size cause metabolic rate in animals to increase more slowly than mass. This ubiquitous biological phenomenon is referred to as metabolic scaling. Mechanistic explanations for interspecific metabolic scaling do not apply for ontogenetic size changes within a species implying different mechanisms for scaling phenomena. Here we show that the Dynamic Energy Budget theory approach of compartmentalizing biomass into reserve and structural components provides a unified framework for understanding ontogenetic and inter-specific metabolic scaling.

Metabolic theory specifies constraints on the metabolic organisation of individual organisms. These constraints have important implications for biological processes ranging from the scale of molecules all the way to the level of populations, communities and ecosystems, with their application to the latter emerging as the field of metabolic ecology. While ecologists continue to use individual metabolism to identify constraints in ecological processes, the topic of metabolic scaling remains controversial.

Projects

Pest mites are a significant threat to the establishment of grain crops. Some species have become more problematic over the last decade as farming practices have changed, while others are proving difficult to control due to tolerance and insecticide resistance issues. The recent emergence of resistance to synthetic pyrethroids and organophosphates in the redlegged earth mite (RLEM) is of particular concern to the Australian grains industry.

Biological phenomena occur across wide scales in space, time, and organisational complexity. Molecules, which are small, quickly transforming units, exhibit new emergent properties when they are arranged into ecosystems. These properties of ecosystems, such as species diversity, distribution, standing biomass, or rates of nutrient turnover involve large spatial and temporal scales, as well as many underlying processes that make their study inherently complex. Integration across disciplines and across levels of biological organisation is one of the grand challenges in biology. Towards this end, novel methods are required so that cross-disciplinary phenomena can be quantified using a common metric. Energy and mass are two universal currencies that are able to cut through the hierarchy of biology, which must be both conserved irrespective to the scale of inquiry.